The generalization performance of deep neural networks in classification
tasks is a major concern in machine learning research. Despite widespread
techniques used to diminish the over-fitting issue such as data augmentation,
pseudo-labeling, regularization, and ensemble learning, this performance still
needs to be enhanced with other approaches. In recent years, it has been
theoretically demonstrated that the loss function characteristics i.e. its
Lipschitzness and maximum value affect the generalization performance of deep
neural networks which can be utilized as a guidance to propose novel distance
measures. In this paper, by analyzing the aforementioned characteristics, we
introduce a distance called Reduced Jeffries-Matusita as a loss function for
training deep classification models to reduce the over-fitting issue. In our
experiments, we evaluate the new loss function in two different problems: image
classification in computer vision and node classification in the context of
graph learning. The results show that the new distance measure stabilizes the
training process significantly, enhances the generalization ability, and
improves the performance of the models in the Accuracy and F1-score metrics,
even if the training set size is small.

通过分析损失函数的特征，本文引入了一种减少过拟合问题的距离称为减少的 Jeffries-Matusita 作为深度分类模型的损失函数，在计算机视觉的图像分类和图学习的节点分类问题中，实验证明新的距离度量显著稳定了训练过程，提高了模型的泛化能力和准确率，并且即使训练集规模较小也有所改善。